Automatic classification of acoustic environment (or acoustic scene) is an essential part of an intelligent hearing device. In the hearing device, the acoustic scene is identified using features of the sound signals collected from that particular acoustic scene. Therewith, parameters and algorithms defining the input/output behavior of the hearing device are adjusted accordingly to maximize the hearing performance. A number of methods of acoustic classification for hearing devices have been described in US-2002/0 037 087 A1 or US-2002/0 090 098 A1. The fundamental method used in scene classification is the so-called pattern recognition (or classification), which range from simple rule-based clustering algorithms to neural networks, and to sophisticated statistical tools such as hidden Markov models (HMM). Further information regarding these known techniques can be found in one of the following publications, for example:                X. Huang, A. Acero, and H.-W. Hon, “Spoken Language Processing: A Guide to Theory”, Algorithm and System Development, Upper Saddle River, N.J.: Prentice Hall Inc., 2001.        L. R. Rabiner and B.-H. Juang, “Fundamentals of Speech Recognition”, Upper Saddle River, N.J.: Prentice Hall Inc., 1993.        M. C. Buchler, Algorithms for Sound Classification in Hearing Instruments, doctoral dissertation, ETH-Zurich, 2002.        L. R. Rabiner and B.-H. Juang, “An introduction to Hidden Markov Models”, IEEE Acoustics Speech and Signal Processing Magazine, January 1986.        S. Theodoridis and K. Koutroumbas, “Pattern Recognition”, New York: Academic Press, 1999.        
Pattern recognition methods are useful in automating the acoustic scene classification task. However, all pattern recognition methods rely on some form of prior association of labeled acoustic scenes and resulting feature vectors extracted from the audio signals belonging to these acoustic scenes. For instance in a rule-based clustering algorithm, it is necessary to set proper thresholds for feature comparisons to differentiate one acoustic scene from other acoustic scenes. These thresholds on feature values are obtained observing a set of audio signals for their characteristics associated with certain acoustic scenes. Another example is an HMM—(Hidden Markov Model) classifier: one adjusts the parameters of a HMM for each acoustic scene one would like to recognize using a set of training data. Then in the actual processing stage, each HMM structure processes the observation sequence and produces a probability score indicating the probability of the respective acoustic scene. The process of associating observations with labeled acoustic scenes is called training of the classifier. Once the classifier has been trained using a training data set (training audio), it can process signals that might be outside the training set. The success of the classifier depends on how well the training data can represent arbitrary data outside the training data.
An objective of the present invention is to provide a method that has an improved reliability when classifying or estimating a momentary acoustic scene.